Mobile apps are essential in daily life, yet they often employ dark patterns, such as visual tricks to highlight certain options or linguistic tactics to nag users into making purchases, to manipulate user behavior. Current research mainly uses manual methods to detect dark patterns, a process that is time-consuming and struggles to keep pace with continually updating and emerging apps. While some studies targeted at automated detection, they are constrained to static patterns and still necessitate manual app exploration. To bridge these gaps, we present AppRay, an innovative system that seamlessly blends task-oriented app exploration with automated dark pattern detection, reducing manual efforts. Our approach consists of two steps: First, we harness the commonsense knowledge of large language models for targeted app exploration, supplemented by traditional random exploration to capture a broader range of UI states. Second, we developed a static and dynamic dark pattern detector powered by a contrastive learning-based multi-label classifier and a rule-based refiner to perform detection. We contributed two datasets, AppRay-Dark and AppRay-Light, with 2,185 unique deceptive patterns (including 149 dynamic instances) across 18 types from 876 UIs and 871 benign UIs. These datasets cover both static and dynamic dark patterns while preserving UI relationships. Experimental results confirm that AppRay can efficiently explore the app and identify a wide range of dark patterns with great performance.

本研究解决了移动应用中黑暗模式检测的不足，提出了一种创新系统AppRay，能够自动识别黑暗模式，从而减少人工探索的需求。研究的关键在于结合任务导向的应用探索和高效的模式检测，实验结果表明AppRay能有效发现广泛的黑暗模式，具有显著的性能优势。

从探索到揭示：移动应用中的黑暗模式检测